Methods. To test for differences between pairs of demographic groups in their affective responses to musical and lyrical features, we formalize alternations between groups as treatments and compute average treatment effects (ATE). In order to account for covariates and reduce bias due to confounding variables, we construct a multi-modal stratified propensity score matching (PSM) model as a quasi-causal analysis of demographic effects. Here, we formalize comments as subjects; the propensity score, defined traditionally as the likelihood of being assigned to a treatment group based on observed characteristics of the subject [1]}, is thus a scaled estimate of the likelihood of a commenter being of a demographic group \(g_i\)  given a set of song features \(f_i\) , or \(P(g_i|f_i)\) . We estimate this probability—the propensity score—via logistic regression on a song's musical and lyrical features, and match data points within stratified deciles of this score to mitigate confounding bias [2]}, [3]}. Within these matched and stratified deciles, we fit separate linear regression models on response valence and arousal against specific song features, weighting and pooling stratum-specific estimated treatment effects to estimate the ATE [4]} and its variance [5]}. Consistent with prior work in musical emotions [6]} and in social psychology on how cultural constructions of gender may account for differences in emotional display [7]}, we observe that response valence and arousal by demographic groups differ in their distributions—for example, as shown in Appendix Section REF , comments made by women are on average higher in both valence and arousal than those made by men. Therefore, we test specifically for standardized change in affective responses across song features within demographic groups. Finally, as in Section REF , we stratify continuous variables in our analyses into fixed-length categories and estimate the ATE of each stratum.
